{"title":"利用机器学习预测肺癌预后的机构间通用性：多中心队列研究（WJOG15121L：REAL-WIND）。","authors":"Daichi Fujimoto , Hidetoshi Hayashi , Kenta Murotani , Yukihiro Toi , Toshihide Yokoyama , Terufumi Kato , Teppei Yamaguchi , Kaoru Tanaka , Satoru Miura , Motohiro Tamiya , Motoko Tachihara , Takehito Shukuya , Yuko Tsuchiya-Kawano , Yuki Sato , Satoshi Ikeda , Shinya Sakata , Takeshi Masuda , Shinnosuke Takemoto , Kohei Otsubo , Ryota Shibaki , Nobuyuki Yamamoto","doi":"10.1016/j.lungcan.2024.107896","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis.</p></div><div><h3>Materials and Methods</h3><p>This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities.</p></div><div><h3>Results</h3><p>Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9–17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88–0.91), 0.85 (0.84–0.87), 0.83 (0.81–0.85), and 0.85 (0.82–0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84–0.92), 0.84 (0.78–0.88), 0.84 (0.76–0.89), and 0.84 (0.75–0.90) at 180, 360, 720, and 1,080 days, respectively.</p></div><div><h3>Conclusion</h3><p>This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.</p></div>","PeriodicalId":18129,"journal":{"name":"Lung Cancer","volume":"194 ","pages":"Article 107896"},"PeriodicalIF":4.5000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of prognosis in lung cancer using machine learning with inter-institutional generalizability: A multicenter cohort study (WJOG15121L: REAL-WIND)\",\"authors\":\"Daichi Fujimoto , Hidetoshi Hayashi , Kenta Murotani , Yukihiro Toi , Toshihide Yokoyama , Terufumi Kato , Teppei Yamaguchi , Kaoru Tanaka , Satoru Miura , Motohiro Tamiya , Motoko Tachihara , Takehito Shukuya , Yuko Tsuchiya-Kawano , Yuki Sato , Satoshi Ikeda , Shinya Sakata , Takeshi Masuda , Shinnosuke Takemoto , Kohei Otsubo , Ryota Shibaki , Nobuyuki Yamamoto\",\"doi\":\"10.1016/j.lungcan.2024.107896\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis.</p></div><div><h3>Materials and Methods</h3><p>This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities.</p></div><div><h3>Results</h3><p>Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9–17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88–0.91), 0.85 (0.84–0.87), 0.83 (0.81–0.85), and 0.85 (0.82–0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84–0.92), 0.84 (0.78–0.88), 0.84 (0.76–0.89), and 0.84 (0.75–0.90) at 180, 360, 720, and 1,080 days, respectively.</p></div><div><h3>Conclusion</h3><p>This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.</p></div>\",\"PeriodicalId\":18129,\"journal\":{\"name\":\"Lung Cancer\",\"volume\":\"194 \",\"pages\":\"Article 107896\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lung Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169500224004306\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lung Cancer","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169500224004306","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的：预测肺癌的预后对于提供最佳医疗服务至关重要。然而，即使使用机器学习，准确预测 IV 期肺癌患者总体预后的方法仍未确立。此外，此类算法的跨机构通用性仍有待探索。本研究旨在建立基于机器学习的、具有机构间通用性的预后预测算法：这项多中心、回顾性、以医院为基础的队列研究纳入了连续的 IV 期肺癌患者，这些患者以 2:1 的比例被随机分为训练队列和独立测试队列。评估算法性能的主要指标是独立测试队列中接收者操作特征曲线下的面积。为了评估算法的机构间通用性，我们使用其他15家机构的数据对算法进行了训练，并调查了算法预测其余机构患者预后的能力：共有 6751 名患者（中位年龄 70 岁）入组，其中 1515 人（22%）出现表皮生长因子受体表达突变。中位总生存期为16.6个月（95%置信区间，15.9-17.5个月）。测试队列中的算法性能指标显示，在 180、360、720 和 1,080 个预测生存天数时，曲线下面积分别为 0.90（95 % 置信区间，0.88-0.91）、0.85（0.84-0.87）、0.83（0.81-0.85）和 0.85（0.82-0.87）。对16种算法进行的性能测试表明，在180天、360天、720天和1,080天时，中位曲线下面积分别为0.87（范围为0.84-0.92）、0.84（0.78-0.88）、0.84（0.76-0.89）和0.84（0.75-0.90）：本研究开发的机器学习算法可准确预测 IV 期肺癌患者的预后，并具有较高的机构间通用性。这可以提高预后预测的准确性，并支持临床环境中的知情和共同决策。
Prediction of prognosis in lung cancer using machine learning with inter-institutional generalizability: A multicenter cohort study (WJOG15121L: REAL-WIND)
Objectives
Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis.
Materials and Methods
This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities.
Results
Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9–17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88–0.91), 0.85 (0.84–0.87), 0.83 (0.81–0.85), and 0.85 (0.82–0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84–0.92), 0.84 (0.78–0.88), 0.84 (0.76–0.89), and 0.84 (0.75–0.90) at 180, 360, 720, and 1,080 days, respectively.
Conclusion
This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.
期刊介绍：
Lung Cancer is an international publication covering the clinical, translational and basic science of malignancies of the lung and chest region.Original research articles, early reports, review articles, editorials and correspondence covering the prevention, epidemiology and etiology, basic biology, pathology, clinical assessment, surgery, chemotherapy, radiotherapy, combined treatment modalities, other treatment modalities and outcomes of lung cancer are welcome.
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